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Best Practices When Starting And Working On A Data Science Project

Several interesting questions were asked recently on Data Science Central. This post addresses the question

“What best practices do you recommend, when starting and working on enterprise analytics projects?”

I have worked as a Data Scientist for 8 years now. This was after completing a PhD on “Design of Experiments for Tuning Optimisation Algorithms”. So I have a formal background in rigorous experiment design for Data Science and have also managed some pretty complex and fast paced projects in sectors including Financial Services, IT, Insurance, Government and Audit.

This post summarises my thoughts on best practice that are heavily based on practical experience as described in my book “Guerrilla Analytics: A Practical Approach to Working with Data”. The book contains almost 100 best practice tips for doing Data Science in dynamic projects where reproducibillty, explainability and team efficiency are critical.